Face detection based on probability of amplitude distribution of local binary patterns algorithm

Face detection and recognition are challenging research topics in the field of robotic vision. Numerous algorithms have been proposed to solve several problems related to changes in environment and lighting conditions. In our research, we introduce a new algorithm for face detection. The proposed method uses the well-known local binary patterns(LBP) algorithm and K-means clustering for face segmentation and maximum likelihood to classify output data. This method can be summarized as a process of detecting and recognizing faces on the basis of the distribution of feature vector amplitudes on six levels, that is, three for positive vector amplitudes and three for negative amplitudes. Detection is conducted by classifying distribution values and deciding whether or not these values compose a face.

[1]  Yizhou Wang,et al.  Face Detection with End-to-End Integration of a ConvNet and a 3D Model , 2016, ECCV.

[2]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[4]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Tao Zhang,et al.  Bootstrapping Face Detection with Hard Negative Examples , 2016, ArXiv.

[6]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Xiaolin Hu,et al.  Joint Training of Cascaded CNN for Face Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Brijesh B. Mehta,et al.  Face Recognition Methods & Applications , 2014, ArXiv.

[10]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[11]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[12]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[13]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[14]  Huaizu Jiang,et al.  Face Detection with the Faster R-CNN , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[15]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[17]  Thierry Bouwmans,et al.  An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos , 2015, VISAPP.

[18]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).